Top 5 Metrics for Evaluating Your Deep Learning Program's GPU Performance - Exxact

Monitoring the right GPU performance metrics can go a long way in helping you train and deploy deep learning applications. GPU utilization is one of the primary metrics to observe during a deep learning training session. This metric is readily accessible through popular GPU monitoring interfaces such as NVIDIA's "NVIDIA-smi". A GPU's utilization is defined as the percentage of time one or more GPU kernels are running over the last second, which is analogous to a GPU being utilized by a deep learning program. Monitoring your deep learning training sessions' GPU utilization is one of the best indicators to determine if your GPU is actually being used. Moreover, monitoring the real-time utilization trend can help identify bottlenecks in your pre-processing and feature engineering pipelines that might be slowing down your training process.